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Transient response characteristic of memristor circuits and biological-like current spikes

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Abstract

Although memristors are mostly considered as a strong candidate for physical realization of synapses in neuromorphic systems, implementation of biologically plausible neurons with memristors creates a great potential to integrate both synapses and neurons on memristor fabrics, such as memristor crossbars. Here we present a discussion regarding transient current response of memristors, and the way we can reproduce biological-like current spikes with very basic memristor circuits. In general, memristor shows a spike-like current response to step changes of the input voltage, which can be used in voltage-excited neuromorphic systems. Accordingly, the properties of current response of single and multiple memristor circuits are investigated in this work. The factors which can affect the shape of the spike-like current response are discussed. Furthermore, it is shown that current spike generated by serially connected memristor and capacitor can exhibit spikes with variety of signaling characteristics. We also discuss about the spike train generated using memristor excited by voltage square wave input. Finally, using memristor and capacitor, a spike-forming circuit is presented in which voltage square wave input is converted to biological-like current spike train.

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References

  1. Chua LO (1971) Memristor—the missing circuit element. IEEE Trans Circuit Theory CT-18:507

    Article  Google Scholar 

  2. Strukov DB, Snider GS, Stewart DR, Williams RS (2008) The missing memristor found. Nature 453:80

    Article  Google Scholar 

  3. Sim H, Choi D, Lee D, Seo S, Lee M, Yoo I, Hwang H (2005) Resistance-switching characteristics of polycrystalline Nb2O5 for nonvolatile memory application. Electron Device Lett 26:292

    Article  Google Scholar 

  4. Muenstermann R, Menke T, Dittmann R, Waser R (2010) Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Adv Mater 22:4819

    Article  Google Scholar 

  5. Pickett MD, Strukov DB, Borghetti JL, Yang JJ, Snider GS, Stewart DR, Williams RS (2009) Switching dynamics in titanium dioxide memristive devices. J Appl Phys 106:074508

    Article  Google Scholar 

  6. Yang JJ, Pickett MD, Li X, Ohlberg DA, Stewart DR, Williams RS (2008) Memristive switching mechanism for metal/oxide/metal nanodevices. Nat Nanotechnol 3:429

    Article  Google Scholar 

  7. Sawa A (2008) Resistive switching in transition metal oxides. Mater Today 11:28–36

    Article  Google Scholar 

  8. Xia Q, Robinett W, Cumbie MW, Banerjee N, Cardinali TJ, Yang JJ, Wu W, Li X, Tong WM, Strukov DB, Snider GS, Medeiros-Ribeiro G, Williams RS (2009) Memristor–CMOS hybrid integrated circuits for reconfigurable logic. Nano Lett 9:3640

    Article  Google Scholar 

  9. Borghetti J, Snider GS, Kuekes PJ, Yang JJ, Stewart DR, Williams RS (2010) ‘Memristive’ switches enable ‘stateful’ logic operations via material implication. Nature 464:873

    Article  Google Scholar 

  10. Linn E, Rosezin R, Kügeler C, Waser R (2010) Complementary resistive switches for passive nanocrossbar memories. Nat Mater 9:403

    Article  Google Scholar 

  11. Jo SH, Chang T, Ebong I, Bhadviya BB, Mazumder P, Lu W (2010) Nanoscale memristor device as a synapse in neuromorphic systems. Nano Lett 10:1297

    Article  Google Scholar 

  12. Pershin YV, Di Ventra M (2010) Experimental demonstration of associative memory with memristive neural networks. Neural Netw 23:881

    Article  Google Scholar 

  13. Pershin YV, La Fontaine S, Di Ventra M (2009) Memristive model of amoeba’s learning. Phys Rev E 80:021926

    Article  Google Scholar 

  14. International Technology Roadmap for Semiconductors (ITRS) (2011) Edition. http://www.itrs.net/Links/2011ITRS/Home2011.htm

  15. Mead C (1990) Neuromorphic electronic systems. Proc IEEE 78(10):1629

    Article  Google Scholar 

  16. Poon CS, Zhou K (2011) Neuromorphic silicon neurons and large-scale neural networks: challenges and opportunities. Front Neurosci 5:108

    Article  Google Scholar 

  17. Modha DS, Ananthanarayanan R, Esser SK, Ndirango A, Sherbondy AJ, Singh R (2011) Cognitive computing. Commun ACM 54:62

    Article  Google Scholar 

  18. Indiveri G, Linares-Barranco B, Hamilton TJ, van Schaik A, Etienne-Cummings R, Delbruck T, Liu S-C, Dudek P, Hafliger P, Renaud S, Schemmel J, Cauwenberghs G, Arthur J, Hynna K, Folowosele F, Saighi S, Serrano-Gotarredona T, Wijekoon J, Wang Y, Boahen K (2011) Neuromorphic silicon neuron circuits. Front Neurosci 5:73

    Google Scholar 

  19. Pershin YV, Di Ventra M (2012) Neuromorphic, digital, and quantum computation with memory circuit elements. Proc IEEE 100:2071

    Article  Google Scholar 

  20. Erokhin V et al (2011) Material memristive device circuits with synaptic plasticity: learning and memory. BioNanoScience 1:24

    Article  Google Scholar 

  21. Zamarreno-Ramos C, Carmunas LA, Pérez-Carrasco JA, Masquelier T, Serrano- Gotarredona T, Linares-Barranco B (2011) On spike-timing dependent plasticity, memristive devices and building a self-learning visual cortex. Front Neuormorphic Eng 5:26

    Google Scholar 

  22. Dan Y, Mu-ming P (2004) Spike timing-dependent plasticity of neural circuits. Neuron 44:23

    Article  Google Scholar 

  23. Gale E, Costello BDL, Adamatzky A (2012) Observation and characterization of memristor current spikes and their application to neuromorphic computation. In: International Conference of Numerical Analysis and Applied Mathematics, vol 1479, pp 1898

  24. Gale E, Costello BDL, Adamatzky A (2014) Spiking in memristor networks. In: Adamatzky A, Chua L (eds) Memristor networks. Springer, Berlin, pp 365

    Chapter  Google Scholar 

  25. Chua L (2013) Memristor, Hodgkin–Huxley, and edge of chaos. Nanotechnology 24:383001

    Article  Google Scholar 

  26. Gale E, de Lacy Costello B, Adamatzky A (2013) Does the D.C. response of memristors allow robotic short-term memory and a possible route to artificial time perception. In: Workshop on Unconventional Approaches to Robotics, Automation and Control Inspired by Nature (UARACIN), Workshop Proceedings of International Conference of Robotics and Automation (ICRA), Fr-Ws-09, pp 22

  27. Gale E, Costello BDL, Adamatzky A (2013) Boolean logic gates from a single memristor via low-level sequential logic. Unconventional computation and natural computation. Springer, Berlin, pp 79–89

    MATH  Google Scholar 

  28. Gale E, Costello BDL, Adamatzky A (2013) Is spiking logic the route to memristor-based computers?. In: Electronics, Circuits, and Systems (ICECS), 2013 IEEE 20th International Conference on, pp 297

  29. Zidan MA, Fahmy HAH, Hussain MM, Salama KN (2013) Memristor-based memory: the sneak paths problem and solutions. Microelectron J 44:176

    Article  Google Scholar 

  30. Eshraghian K, Kavehei O, Cho KR, Chappell JM, Iqbal A, Al-Sarawi SF, Abbott D (2012) Memristive device fundamentals and modeling: applications to circuits and systems simulation. Proc IEEE 100:1991

    Article  Google Scholar 

  31. Eshraghian K, Cho KR, Kavehei O, Kang SK, Abbott D, Kang SMS (2011) Memristor MOS content addressable memory (MCAM): hybrid architecture for future high performance search engines. IEEE Trans Very Large Scale Integr VLSI Syst 19:1407

    Article  Google Scholar 

  32. Kavehei O, Cho KR, Lee SJ, Kim SJ, Al-Sarawi S, Abbott D, Eshraghian K (2011) Fabrication and modeling of Ag/TiO2/ITO memristor. In: In Circuits and Systems (MWSCAS), 2011 IEEE 54th International Midwest Symposium on IEEE, pp 1–4

  33. Saudargiene A, Porr B, Wörgötter F (2004) How the shape of pre-and postsynaptic signals can influence STDP: a biophysical model. Neural Comput 16:595

    Article  MATH  Google Scholar 

  34. Snider GS (2008) Cortical computing with memristive nanodevices. SciDAC Rev 10:58

    Google Scholar 

  35. Pérez-Carrasco JA, Zamarreño-Ramos C, Serrano-Gotarredona T, Linares-Barranco B (2010) On neuromorphic spiking architectures for asynchronous STDP memristive systems. In: Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on, pp 1659

  36. Ebong I, Mazumder P (2010) Memristor based STDP learning network for position detection. In: Microelectronics (ICM), 2010 International Conference on, pp 292

  37. Herz AVM, Gollisch T, Machens CK, Jaeger D (2006) Modeling single-neuron dynamics and computations: a balance of detail and abstraction. Science 314:8085

    Article  MathSciNet  MATH  Google Scholar 

  38. Izhikevich EM (2010) Hybrid spiking models. Philos Trans R Soc A 368:50615070

    Article  MathSciNet  MATH  Google Scholar 

  39. Brüderle D (2011) A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems. Biol Cybern 104:263296

    Article  Google Scholar 

  40. Arthur JV, Boahen KA (2011) Silicon-neuron design: a dynamical systems approach. IEEE Trans Circuits Syst 58:10341043

    Article  MathSciNet  Google Scholar 

  41. Cruz-Albrecht JM, Yung MW, Srinivasa N (2012) Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Trans Biomed Circuits Syst 6:246

    Article  Google Scholar 

  42. Crane HD (1962) Neuristor—a novel device and system concept. Proc IRE 50:2048

    Article  Google Scholar 

  43. Pickett MD, Medeiros-Ribeiro G, Williams RS (2013) A scalable neuristor built with Mott memristors. Nat Mater 12:114–117

    Article  Google Scholar 

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Correspondence to Arash Ahmadi.

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Feali, M.S., Ahmadi, A. Transient response characteristic of memristor circuits and biological-like current spikes. Neural Comput & Applic 28, 3295–3305 (2017). https://doi.org/10.1007/s00521-016-2248-1

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